The following relates to recommendation systems. It finds particular application in recommendation systems utilizing missing value projections provided via latent semantic indexing techniques.
In one application, a web-based tool allows a user to enter a customer's information and associated workflow requirements and/or constraints through a dynamic questionnaire process. A set of workflow configurations that possibly satisfy the customer's requirements are auto-generated. Finally, the customer will choose the most suitable one among the auto-generated workflows.
In one approach, the customer's constraints, the generated workflows, and final customer choice are recorded by the tool as a “case log” which can be identified by a unique case identification code and stored in the case database. Based on these collected case logs, a production printing workflow recommendation system can provide new incoming cases with suggested workflow configurations. The workflow recommendation system can discover hidden knowledge from existing case logs to enhance the core knowledge model and questionnaires of the workflow generation tool. In addition, the workflow recommendation system can significantly improve the efficiency and accuracy of current workflow generation tools by narrowing down the workflow search scope for new cases that are similar to existing ones.
However, there are several drawbacks to this approach. The major difficulty of designing a workflow recommendation system is due to the high incompleteness of data received. In some instance, many case constraints have missing values because of customers' laziness or incapability to answer constraint related questions. Most reported approaches of dealing with data incompleteness (e.g., mean/median estimation, regression, interpolation, etc.) fall into the category of missing value prediction. However, missing value prediction techniques are limited in that they achieve adequate performance only under scenarios with only a few missing values and hence are not suitable for applications where a large number of constraints have missing values.
Another approach, collaborative filtering, is also adopted by some recommendation systems to predict the missing recommendation scores of customers towards different products. This technique focuses only on recommendation score prediction and is not directly applicable for customer constraints (e.g., requirements) prediction needed in such applications.
In order to remedy this problem, alternative systems and methods need to be employed to provide accurate and useful recommendations based on incomplete data sets.